20 citations · 41 across the 9 of their papers we have counts for
6 papers · 1 filter
QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression
Kedeng Tong, Yaojun Wu, Yue Li +3
Learned image compression has exhibited promising compression performance, but variable bitrates over a wide range remain a challenge. State-of-the-art variable rate methods compro…
Learned Block-based Hybrid Image Compression
Yaojun Wu, Xin Li, Zhizheng Zhang +2
Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. F…
FAN: Frequency Aggregation Network for Real Image Super-resolution
Yingxue Pang, Xin Li, Xin Jin +4
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achiev…
Learned Video Compression with Feature-level Residuals
Runsen Feng, Yaojun Wu, Zongyu Guo +3
In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the curr…
3-D Context Entropy Model for Improved Practical Image Compression
Zongyu Guo, Yaojun Wu, Runsen Feng +2
In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthene…
Generative Memorize-Then-Recall framework for low bit-rate Surveillance Video Compression
Yaojun Wu, Tianyu He, Zhibo Chen
Applications of surveillance video have developed rapidly in recent years to protect public safety and daily life, which often detect and recognize objects in video sequences. Trad…